Team
operators who want AI work delivered, not configured
Managed AI
Last updated: September 2026
Managed AI employees — not managed AI as an IT service
Managed AI often means an IT programme. Here it means a named employee on the queue.
Rebotify runs roles for calls and inbox work inside your existing tools.
Human approval before send. Australian team running the service.
Direct answer
Managed AI often means an enterprise IT programme.
Here it means managed AI employees: named roles that own phone and inbox work in your existing systems, with human approval before send, and Australians running the service.
Send the work your team keeps debugging. Mia maps what Rebotify can run and tune for you.
Runs inside
Gmail
Outlook
Slack
HubSpot
Salesforce
Notion
Zendesk
Google Drive
Workday pressure
Start with what your team already says.
Your AI employee does not score AI interest.
She scores the queue: what piles up, who gets chased, and what still needs approval.
The first version must clear visible work.
Workday sentence
They say: diy agent platforms need a team to babysit them, inbox triage and reply drafting.
Answer that pressure first.
Where it gets stuck
DIY agent platforms need a team to babysit them: Zapier, Make, n8n, Lindy dashboards eat headcount.
Every prompt tweak, every API change, every edge case lands on your team.
That is not a platform — it is outsourced staff you still manage.
What cannot go wrong
You want to build your own agents in-house and own every prompt and integration.
What stays human
You approve every sensitive decision: Customer-facing, legal, financial, and unusual actions wait in the queue for a named human to sign off.
First useful version
The team approves work and reviews metrics — nobody on payroll is debugging prompts.
Work first
What changes when this work gets handled.
The question is simple.
Can this work be cleared with less cost, less waiting, fewer misses, and less manager attention?
Work to clear
What your team gets back
The team approves work and reviews metrics — nobody on payroll is debugging prompts.
Impact
Why it is worth doing
To first drafts in the approval queue.
Current cost
What it costs now
Zapier, Make, n8n, Lindy dashboards eat headcount.
Every prompt tweak, every API change, every edge case lands on your team.
That is not a platform — it is outsourced staff you still manage.
Human approval
Where people stay in charge
You approve every sensitive decision: Customer-facing, legal, financial, and unusual actions wait in the queue for a named human to sign off.
Work in motion
What it looks like when the work is moving.
Week-one outputs. Drafted for review before send.
EXAMPLE · 01
Inbox triage and reply drafting
The operator runs the morning queue, your team approves what goes out, and weekly tuning closes the loop on missed categories.
EXAMPLE · 02
Document checks and exception flagging
Contracts, invoices, or applications get a first-pass review inside the operator-managed playbook; humans review only the flags.
EXAMPLE · 03
Weekly reporting and anomaly summaries
Numbers, narrative, and call-outs prepared by the AI employee; the operator tunes the report template as the business changes.
48-hour build
What ships in the first window.
01
Named operator, live in 48 hours
We scope the workflow, wire it to your tools, and deploy a working AI role within two business days.
No weeks of planning.
No discovery tax.
We go live and tune in place.
02
Weekly tuning and incident response
Each week we review what the AI did, what it missed, and what changed in your process.
Edge cases become rules.
New fields, policy shifts, and tool updates get handled the same week.
03
Daily monitoring and guardrails
The AI runs every day.
We watch an error log for failed steps, stalled queues, and upstream changes — a bounced email, a CRM field that renamed itself, a source system that timed out.
You see a plain-language summary; we own the fix.
Human approvals stay in the queue where you set them, with a named approver for anything sensitive.
04
Three ways to pay, month-to-month contract
Flat monthly, per-completed-task, or by outcome.
You pick the model that matches the work, month-to-month.
Send us the bottleneck and rough volume to scope the workflow and confirm the price.
Human control
The employee prepares the work. People keep judgment.
You approve every sensitive decision
Customer-facing, legal, financial, and unusual actions wait in the queue for a named human to sign off.
Scoped tool access
The AI employee gets only the permissions its job needs.
Access can be revoked any day, same day.
Nothing trains shared models
We do not train shared models on your data.
Tuning happens inside your engagement, not a shared pool.
What managed does not include
This is not 24/7 live incident response, and Rebotify does not hold or claim SOC 2, HIPAA, or PCI certification.
Monitoring runs daily, tuning runs weekly, and anything sensitive waits for a named human to sign off.
If a workflow needs certified compliance coverage, say so before scope is set.
Governance follows a recognized reference, not a badge
The operating loop — scope, connect, monitor, human sign-off, tune — tracks the structure the NIST AI Risk Management Framework describes for governing, mapping, measuring, and managing AI risk.
That is a design reference we hold ourselves to, not a certification Rebotify has been awarded.
Do not start here if
- You want to build your own agents in-house and own every prompt and integration.
- You want a platform license you can resell to your customers.
- You want one-off project delivery with no ongoing operating relationship.
A good first week looks like
- The team approves work and reviews metrics — nobody on payroll is debugging prompts.
- Misses become updates to the playbook within the same week they happen.
- When a tool changes (new CRM field, new approval policy), the operator handles the change.
Work scorecard
Before you hire for it, send us the stuck work.
Mia checks the cost, risk, what needs sign-off, and whether an AI employee can clear the first version.
If this is cheaper or safer with a person, the scorecard says that.
WORK + APPROVAL SCORECARD
A short check for cost, speed, quality, risk, and the first safe version.
Work
What keeps piling up?
Replies, reports, checks, handoffs, document chases, approvals, or follow-up that keeps coming back.
Cost
What does it cost now?
Staff time, manager attention, customer wait time, rework, missed follow-ups, or lost revenue.
Quality
What would make it useful?
Better drafts, faster turnaround, fewer errors, cleaner handoffs, and less chasing from managers.
Control
What still needs human approval?
Customer promises, pricing, refunds, legal language, financial decisions, or anything that can damage trust.
Output: work to clear, current cost, what needs sign-off, pricing options, and the smallest useful test.
What is managed AI?
Managed AI is a service where the provider builds, runs, and tunes the AI role for you, instead of selling you a platform to configure yourself. Rebotify scopes the workflow, wires the tools, monitors it daily, tunes it weekly, and puts sensitive decisions in front of a named human. You review the work; you don’t administer the software.
How is it different from an AI platform?
A platform gives your team a dashboard, credits, and prompts to maintain. Managed AI gives your team a role that already runs, with Rebotify accountable for keeping it running. There is no seat login for your staff to learn and no prompt library for them to own.
Who owns errors?
Rebotify does. We monitor an error log daily for failed steps and stalled queues and fix what breaks. Anything that reached a customer, or could have, is reviewed the same week and turned into a playbook rule so it does not repeat.
Can it access all company data?
No. The AI employee gets the specific, scoped permissions its one workflow needs — a shared inbox label, a CRM object, a helpdesk queue — not blanket account access. Scopes can be revoked the same day if anything changes.
What happens after 48 hours?
The first workflow goes live with real drafts in your approval queue. After that, Rebotify keeps watching it daily and tuning it weekly: misses get reviewed, the playbook gets updated, and a second workflow only gets scoped once the first one has earned trust.
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48-HOUR START
Tell us the queue that keeps slipping. Leave with the first AI employee scope.
Stop babysitting automation
Send the work your team keeps debugging.
Mia maps what Rebotify can run and tune for you.